If you’re running modern applications on AWS, you already have access to more data than ever: metrics, logs, traces, and events. The real advantage comes from turning that data into actionable insights that drive continuous improvement.

Today’s challenge occurs when something breaks; teams still spend too much time connecting the dots. Pulling data from different tools, correlating signals, and trying to figure out what changed. That gap between insight and action is where time is lost, and customer impact grows.

Dynatrace and AWS are working together to close that gap.

With the introduction of AWS DevOps Agent and deeper integrations across Dynatrace and AWS services like Kiro, the experience moves beyond monitoring into AI-powered observability. It becomes a connected system that detects issues, investigates them, and feeds directly into how teams fix and improve software.

How we got here

At AWS re:Invent, AWS introduced DevOps Agent, a new type of AI agent built to investigate and resolve issues across cloud environments. Instead of relying on manual troubleshooting, the agent works continuously across services to understand what changed and why. Dynatrace has been part of that effort since the start. After all, if these agents are going to be effective, they need real production context. That’s the Dynatrace specialty.

That collaboration evolved quickly. Sharing observability data is now a two-way integration. Dynatrace detects and understands issues. AWS DevOps Agent investigates them. The results flow directly back into Dynatrace for a complete view of what happened and what to do next.

The next iteration of the Clouds SRE app

As customers started using the initial implementation, one thing became clear. The foundation was there, but there was an opportunity to make the experience more streamlined, more transparent, and more aligned with how teams actually operate at scale.

With the introduction of the Clouds SRE app, that experience has evolved in a meaningful way.

Instead of requiring users to manually configure multiple workflows, onboarding is now guided. Teams can get started faster without needing to define everything upfront. What used to take several workflows is now simplified into a more focused, purpose-built experience across AWS.

Visibility is another big step forward. Previously, there was limited insight into what agents were doing during an investigation. Now, teams have transparent tracking into agent activity, including approvals, notifications, and the ability to automatically re-run stalled investigations. That shift alone gives teams more confidence in how work is executed.

Routing and management have also become much more intuitive. Rather than managing workflows behind the scenes, teams can now use interaction profiles directly in the UI to control how agents are routed, filtered, and managed. It brings that control closer to where teams already operate.

And importantly, the scope of what agents can do has expanded. What was once limited to investigations now includes mitigation actions as well, allowing teams to move from insight to action without changing context.

Finally, there’s a stronger focus on outcomes. With built-in executive summaries and efficiency metrics, teams can now understand the impact of these workflows in real terms, not just activity.

Taken together, this is a shift from a workflow-centric model to an experience that is guided, observable, and outcome-driven.

From investigation to resolution

Because the integration is two-way, everything stays connected. Findings from AWS DevOps Agent flow directly back into Dynatrace. Root cause, impacted services, and recommended fixes are all visible in a single place.

Teams no longer need to switch between tools or reconstruct the story themselves. The full path from detection to resolution is already laid out.

For teams running distributed systems on AWS, this changes daily operations. Instead of spending time figuring out where to look, teams can focus on fixing the issue and preventing it from happening again.

Bringing that context to developers with Kiro

This is where the story extends beyond operations. Kiro, AWS’s agentic development environment, brings that same production context directly into the developer workflow.

Instead of waiting for a handoff from operations, developers can access real production insights while they are building and fixing code. They can see exactly what failed, understand the root cause, and apply fixes with the same context that was used during investigation.

This removes one of the biggest sources of friction in software delivery. Developers are no longer dependent on separate teams to translate production issues. They are working from the same data in real time.

Now we have a closed loop: Dynatrace detects the issue. AWS DevOps Agent investigates it. Kiro brings that insight directly into the codebase where it can be resolved and improved.

That closes the gap between production and development in a way that was not possible before.

What this means for you

If you are running applications on AWS today, these developments can result in:

• Less time spent correlating data across tools
• Faster and more consistent incident resolution
• Fewer handoffs between operations and development
• Direct access to production context during development

Most importantly, it shortens the feedback loop. Issues are not just detected faster; they can be understood and resolved faster, and improvements can be applied to the code without delay.

Getting started

If you are already using Dynatrace on AWS, the next step is to connect these workflows.

Start by enabling the AWS DevOps Agent integration with Dynatrace to bring automated investigation into your environment. From there, extend that same production context into developer workflows with Kiro so your teams can act on insights directly.

This is the fastest way to move from insight to action and start seeing the value in day-to-day operations.

The post Moving from insight to action: How Dynatrace and AWS are reshaping cloud operations appeared first on Dynatrace news.

Every developer knows the feeling: You’re in your IDE when something breaks. Error rates spike, alerts fire, and suddenly you’re out of the flow. Michael Kobush, Performance Engineer III at the National Association of Insurance Commissioners (NAIC®), wanted to eliminate the gap between development and runtime. Instead of switching tools or waiting on SRE support, NAIC set out to bring production insight directly into the developer workflow.

Let’s take a look at how NAIC embedded real-time observability directly into their development workflow and reduced investigation time to a few minutes.

The problem: Context switching kills developer productivity

Developers lose time the moment they leave their IDE, jumping between views of logs, metrics, and traces simply to understand what has changed.

For NAIC, this friction was slowing down their teams. Developers didn’t have access to production context, creating a dependency on SRE teams whenever investigations were needed. An analysis that should have taken minutes routinely took 45 minutes to an hour. Root-cause identification required manual correlation across multiple systems, a process that was neither scalable nor sustainable.

At Dynatrace Perform 2026, Kobush demonstrated how his team uses Kiro and Dynatrace at NAIC: Real-time observability in your IDE: How NAIC uses Kiro powers to drive developer productivity

The solution: Kiro powers and intelligent observability

Kiro is AWS’s agentic AI-powered IDE that takes a spec-driven approach to software development by turning natural language prompts into structured requirements, architecture designs, and implementation tasks to carry code from prototype to production.

NAIC installed the Dynatrace power for Kiro, one of Kiro’s installable powers that
dynamically connect domain-specific tools and context to the agent. Once connected, Kiro gives developers and AI agents access to Dynatrace data and insights, helping them pinpoint root causes and receive remediation recommendations directly in their workflow.

No switching between tools. No waiting on another team.

The aha moment: Root-cause analysis in minutes, not hours

The first prompt NAIC ran after connecting Kiro to Dynatrace set the tone for everything that followed. Kobush typed a single line into Kiro: “Tell me about problem P-18576.”

Within 30 seconds, Kiro returned a full problem summary with details and recommendations, pulling everything from Dynatrace automatically. Then, he pushed further: “Give me a really deep dive root-cause analysis of what happened.”

In under two minutes, Kiro returned a full root-cause analysis correlating telemetry, infrastructure signals, historical incidents, and the current problem from Dynatrace into a structured response that included:

• An executive summary
• Detailed problem context
• Infrastructure analysis
• Technical root-cause analysis
• Remediation strategies
• Conclusions and next steps

A preliminary assessment that would have previously taken 45 minutes to an hour was now done in minutes. More importantly, it wasn’t just faster; it gave the team a clear, connected view of how services, infrastructure, and dependencies contributed to the issue.

Beyond root cause: Automation across the entire workflow

What makes this more than just a faster diagnostic tool is how NAIC extended Kiro’s capabilities to automate the full incident response workflow.

Using Kiro’s steering files feature, NAIC configured Kiro to automatically generate a structured Markdown file whenever a root-cause analysis was completed. That file includes:

• Relevant DQL queries used during the investigation
• Direct links to the Dynatrace dashboards and data sources that surfaced the issue
• A clear summary of findings

With a Targetprocess MCP also connected, Kiro can take that analysis and populate a ticket directly, automatically loading all relevant context and sending it to the development team. For NAIC, this means the handoff from investigation to remediation is essentially hands-off. This level of automation doesn’t just save time; it creates consistent, repeatable workflows with built-in guardrails. Every incident gets the same structured, data-rich documentation, regardless of who’s investigating it or when.

This isn’t just about faster incident response. It changes how teams build and release software—giving developers immediate feedback on how their changes behave in real environments.

Proactive alerting: Catching problems before they crash

Root-cause analysis after the fact is valuable. With observability embedded directly into the workflow, teams can detect issues earlier in development and respond faster in production, closing the gap between building and operating software.

After noticing that a specific process had crashed, Kobush asked Kiro to set up an alerting profile that would trigger both before the crash, based on stress signals visible in the logs, and at the point of the crash. Kiro analyzed historical log data, identified pre-crash indicators, and built the alert profile automatically.

The result: NAIC’s team now receives early warning signals before a process fails, giving engineers time to intervene rather than react.

This shift from reactive to proactive operations is central to what the Dynatrace and AWS partnership enables. When observability data is embedded in the developer workflow rather than siloed in a separate platform, the entire engineering organization is better equipped to prevent incidents, not just resolve them.

Debugging a sneaky production bug

Perhaps the most telling story from NAIC’s experience with Kiro occurred during a routine error-rate investigation.

An application error rate had increased unexpectedly. Kobush asked Kiro to investigate. Two minutes later, Kiro identified the culprit: A developer had left debug code in the development environment, and it had made its way into production. Every time a user triggered that code path, it threw errors.

When Kobush sent the Markdown report to the developer, the response was immediate: “How did you find that? I’ve been looking for that.”

Kiro leveraged correlated logs, traces, systems context, and historical behavior from Dynatrace to pinpoint exactly where the issue originated.

Start embedding observability into your development workflow

NAIC’s experience highlights a broader shift: When developers, AI assistants, and systems all operate from the same runtime context, debugging becomes faster, releases become safer, and teams spend less time chasing issues and more time building.

The broader message from Kobush is simple: “I’m not a developer. I have a degree in biology and a minor in chemistry… But this, to me, is a game changer in the observability space. I can do things in seconds that would take me hours.”

The productivity gap between observability data and developer action is a solvable problem.

For DevOps engineers, SREs, and platform teams looking to accelerate incident resolution, reduce context switching, and move from reactive troubleshooting to proactive operations, the Dynatrace and Kiro integration offers a practical, immediately actionable path forward.

For developers, this means fewer interruptions, faster answers, and the ability to stay in flow, even when issues arise.

The post From reactive to proactive: How NAIC embedded AI‑powered observability directly into the IDE appeared first on Dynatrace news.

A new chapter of digital innovation is underway.

With the emergence of agent-driven services and generative capabilities, organizations now have new ways of building, operating, and improving digital products. These technologies open the door to richer customer experiences, faster feedback cycles, and continuous improvement powered by real-time insight.

At AWS Summits, Dynatrace is showcasing how our work with Amazon Web Services can help teams explore ways to turn agentic and generative innovation into measurable outcomes. Together, our teams have been delivering platform capabilities and deep integrations that allow customers to take advantage of these new AWS services at scale.

If you are attending an AWS Summit, stop by the Dynatrace booth to see agentic and generative innovation in action.

Turning new AWS capabilities into real-world outcomes

Dynatrace and AWS share a common mission to support customers as they innovate more efficiently and work toward better digital experiences across cloud-native and modern application environments.

Through close collaboration, Dynatrace has enabled customers to adopt new AWS services with confidence from the moment they become available. Dynatrace provides the intelligence layer that connects data across applications and infrastructure, allowing teams to better understand their user behaviors and application interactions, which can help inform business decisions and improvements.

As AWS introduces agent-based services and managed generative platforms, Dynatrace helps customers fully realize their value by connecting insight to action.

Bringing AWS-native signals into one view with Dynatrace Clouds app

A key part of unlocking this opportunity on AWS is access to the native signals that AWS services already produce.

Dynatrace Cloud Operations brings Amazon CloudWatch metrics and AWS service data points directly into the Dynatrace platform for a richer context. This allows teams to work with AWS-native telemetry alongside application behavior, user experience, and business signals in a single view.

With the Clouds app, customers can see CloudWatch metrics from AWS services in context with applications and workloads, connect AWS service signals using Dynatrace SmartScape® topology, and apply consistent analysis and automation across AWS native data and Dynatrace collected data.

At the AWS Summits, Dynatrace will demonstrate how the Clouds app enables shared visibility across AWS environments by connecting managed Dynatrace environments to Dynatrace SaaS and AWS services. This shared visibility becomes especially powerful when combined with agent-driven services and Amazon Bedrock, allowing agents and automated workflows to operate using trusted AWS data together with Dynatrace Intelligence.

See agentic and generative innovation in real‑world cloud environments

At the AWS Summits, Dynatrace will demonstrate how AWS services and Dynatrace Intelligence work together to enable a new way of building and evolving digital products. Live demos and hands-on conversations will show how insight flows naturally across teams and systems.

AWS DevOps Agent and continuous product improvement

With AWS DevOps Agent and Dynatrace, teams can understand in real time how changes impact users and business outcomes. Dynatrace provides trusted context from production environments that feed into agent-driven workflows, helping teams learn faster and deliver higher quality experiences with every release.

Amazon Bedrock and Bedrock AgentCore at real-world scale

With Amazon Bedrock and Bedrock AgentCore, teams are building agents that reason across information and act on behalf of users. Dynatrace enables customers to understand how these services behave in real-usage scenarios and scale generative capabilities within their products.

Kiro and Kiro Powers accelerating innovation from code to customer

Dynatrace connects live production insight back into agent-assisted development workflows, allowing teams to validate ideas using real usage and performance signals and shorten the cycle from idea to impact.

Dynatrace MCP connecting context across agent-driven systems

Dynatrace Model Context Protocol technology enables secure and scalable sharing of system context across agent-driven ecosystems. At the summit, see how MCP connects managed Dynatrace environments, Dynatrace SaaS, and Amazon Bedrock to support coordinated-intelligent behavior at cloud scale.

Amazon SageMaker and continuous product improvement

With Amazon SageMaker and Dynatrace, teams gain insight into how models perform in live environments and refine AI-driven features over time based on real-usage patterns.

Join us at the AWS Summits

Dynatrace and AWS have been working together to deliver the platform capabilities, integrations, and shared intelligence that support this opportunity in real‑world environments today.

We’ll be onsite at multiple AWS Summits across North America, EMEA, and APJ. Visit the Dynatrace booth to see live demos and connect with experts shaping the next generation of digital products. See us at an AWS Summit near you.

Dynatrace, Dynatrace SmartScape®, Dynatrace Model Context Protocol, Dynatrace MCP, and Dynatrace Intelligence are trademarks or registered trademarks of the Dynatrace, Inc. group of companies. Amazon Web Services, AWS, Amazon Bedrock, Amazon SageMaker, and CloudWatch are trademarks of Amazon.com, Inc. or its affiliates. All other trademarks are the property of their respective owners.

The post Unlocking a new era of digital innovation with Dynatrace and AWS appeared first on Dynatrace news.

Teams can transition from setup to insights in minutes using a lightweight OTLP configuration and ready-made dashboards.

Agentic observability is evolving

Agentic AI systems are quickly moving from proof-of-concept to production, giving customers the ability to automate complex workflows, invoke a variety of different tools and APIs, and coordinate tasks across multiple services. However, traditional monitoring overlooks critical AI-specific signals, such as token consumption, model behavior, and guardrail outcomes. Teams struggle to trace non-linear agent flows, establish baselines for dynamic systems, and maintain predictable costs as usage scales. Without purpose-built observability, organizations risk degraded experiences, higher costs, and compliance gaps as agent complexity grows.

As agentic AI moves from pilot programs to production, organizations are automating complex, cross-system workflows with Amazon Bedrock AgentCore. However, most monitoring stacks weren’t designed for emergent, tool-driven behaviors and, therefore, leave blind spots around correctness, safety, and cost. Teams struggle to trace non-linear flows, establish baselines for dynamic systems, build agentic workflows, and keep token-driven spend under control as usage scales.

The observability gap in AI agent deployments

While AI agents offer significant benefits, including improved employee productivity, increased efficiency, and competitive advantage, among others, an observability gap remains, creating the following challenges:

• Complex multi-step workflows
  AI agents run non-linear, multi-system sequences with inter-agent dependencies, making data flow and responsibility hard to trace. This obscures where time is spent and who is responsible for failures in the chain.
• Limitations of traditional metrics
  Basic operational metrics often overlook AI reasoning errors and quality issues that don’t significantly affect CPU or p95 latency. Without AI-specific telemetry, subtle degradations often slip through.
• Continuous underlying agent and LLM model version changes
  Your system might be robust today, but upstream model and version updates can alter behavior, latency, and costs, forcing continuous adaptation to prevent regressions and incidents. Proactive detection of model-induced changes is crucial to maintaining stable quality and safety over time.
• Scalability and quality challenges
  As deployments grow, telemetry volume and coordination overhead surge while token usage and API calls remain untracked. This breaks cost predictability and quality control, leading to issues such as hallucinations and model drift. Multi-agent logic evolves constantly, so “normal” is a moving target. Baselines drift, complicating anomaly detection and root-cause analysis.

Without addressing these challenges, organizations face risks, from degraded user experiences and spiraling costs to compliance violations and reputational damage.

New enhancements for teams building with Amazon Bedrock AgentCore

The new Dynatrace AI Observability app embeds Amazon Bedrock AgentCore observability into a dedicated end-to-end experience, featuring out-of-the-box analytics, auto-instrumentation, targeted GenAI metrics, debugging flows, and ready-made dashboards to address all observability gaps in agent deployments. Support is available for over 20 technologies, including Amazon Bedrock, OpenAI, Gemini/Vertex, Anthropic, and LangChain.

These enhancements enable teams to take advantage of the following benefits:

• End-to-end distributed tracing
  Trace every interaction from user prompt to model reasoning to tool calls, so you can pinpoint bottlenecks, errors, or costly loops in seconds. Filter by model, provider, token usage, latency, and more to accelerate root-cause analysis.
• Enriched GenAI telemetry data, out of the box
  Each LLM and tool invocation emits spans with prompts, completions, token counts (for both prompts and completions), finish reasons, model IDs, latency, and errors, utilizing GenAI semantic attributes. Orchestration layers (for example, actions, HTTP durations, and step names) are captured for the complete workflow context.
• Cost, performance, and safety insights
  Use intelligent forecasting to detect cost and performance anomalies in token consumption and latency. Monitor guardrails for toxicity, PII, and denied topics to build trust and meet compliance requirements.
• Simple OTLP setup, fast time to value
  AgentCore already emits telemetry; simply register the OpenTelemetry export to Dynatrace once. Use your Dynatrace OTLP endpoint and token, and you’re streaming signals into the Dynatrace Grail® data lakehouse with no code rewrites. Ready-made dashboards for Amazon Bedrock let you verify ingestion and gain instant insights.

What’s next

We’re investing in a deeper Amazon Bedrock model and provider insights, expanded guardrail analytics, and additional automation so you can attach remediation playbooks to cost or safety anomalies.

Additionally, we’ll introduce a new agent visualization and topology experience that visualizes your AgentCore agents, LLM services, tool backends, and dependencies, allowing you to understand real-time topology and data flows across the entire stack.

Navigate from the topology map to traces to follow agent behavior step-by-step across services, protocols, and external calls, pinpointing hotspots, ownership, and blast radius more quickly.

Expect tighter integrations with popular orchestration frameworks and more dashboards for common agent patterns, such as retrieval, multi-agent collaboration, and tool-heavy workflows.

Get started with Dynatrace AI Observability for Amazon Bedrock AgentCore agents

Ready to learn more? Have a look at our GitHub repository.

Start instrumenting your agents today. Open the Amazon Bedrock AI Observability dashboard in Dynatrace to verify telemetry and begin your analysis.

The post Announcing Amazon Bedrock AgentCore Agent Observability appeared first on Dynatrace news.

Enterprise adoption of generative AI is showing no signs of slowing down, and it’s easy to understand why; organizations in every vertical aim to reap its benefits, including increased efficiency, routine task automation, and content generation, ultimately creating a competitive advantage. To better help organizations maximize the benefit and full potential of generative AI, Dynatrace has achieved the Amazon Web Services Generative AI (GenAI) Competency.

With this milestone, Dynatrace reinforces its position as a leading observability partner, backed by a proven track record of innovation and customer success on AWS. Building on its achievement of earning the AWS Machine Learning Competency, Dynatrace continues to drive advancements in generative AI.

Weighing the importance of this milestone for Dynatrace customers

This competency is more than just a badge; it’s a validation of how Dynatrace can help organizations safely, efficiently, and cost-effectively adopt generative AI in their business. AWS awards these competencies after rigorous technical validation and proven customer success. This means organizations can trust that Dynatrace solutions are designed to deliver measurable outcomes on AWS.

For existing customers, this competency reaffirms the Dynatrace commitment to continued innovation alongside AWS. This ensures the Dynatrace AI-powered observability platform evolves with the latest advancements in AI, future-proofing organizations’ existing investments as generative AI capabilities become core to modern cloud workloads.

For new customers, Dynatrace provides a trusted, proven foundation for observability and AI adoption on AWS. Whether an organization is exploring GenAI for customer engagement, automation, or new digital experiences, Dynatrace ensures these systems are reliable, secure, and optimized at every step.

Looking ahead with AI-powered observability on AWS

As organizations increasingly adopt generative AI, observability becomes a critical enabler. By leveraging Dynatrace causal AI, predictive insights, and seamless AWS integrations, organizations can maintain control over costs, risks, and performance while driving innovation, enhancing competitive advantage, and delivering exceptional customer experiences.

Whether you’re building, scaling, or fine-tuning GenAI application, Dynatrace and AWS Bedrock empower you to transform your observability. With end-to-end visibility into AI workloads, their interactions in full context of your business, and cloud-native applications, you can optimize performance, troubleshoot effectively, and maximize the value of your GenAI investments with greater confidence and precision.

Learn more

• Davis AI
• Dynatrace for Amazon Bedrock
• Explore the Bedrock Monitoring Playground to get hands-on with real-time dashboards that reveal how your Amazon Bedrock models behave in production.
• Install the AI Observability app to integrate Dynatrace’s AI Observability app into your environment to monitor GenAI workloads.
• Stop by booth #575 at AWS re:Invent 2025 to see Dynatrace in action.

The post Dynatrace achieves AWS Generative AI Competency: A new milestone in observability and AI appeared first on Dynatrace news.

A generational technology shift is afoot—one where AI-powered workloads are the new currency of innovation. Agentic applications—built on foundation models, APIs, and autonomous workflows—are transforming how businesses create, deliver, and scale value.

Enterprise leaders are no longer asking if AI will play a central role in their strategy. Rather, they’re asking how fast they can operationalize AI to drive personalization, automation, and competitive advantage.

From observability to actionable intelligence

The key to unlocking AI’s full potential lies in understanding what your data is telling you. AI-native organizations don’t just need visibility; they need context, causality, and automation across their digital systems.

This is where Dynatrace excels. By combining full-stack observability with causal AI and advanced analytics, Dynatrace can help enterprises move beyond dashboards toward self-healing systems, intelligent automation, and AI-powered experiences that continuously improve.

Enhance agentic solutions with Dynatrace through the AWS Agentic AI Marketplace

To accelerate your journey in this AI-driven era, Dynatrace is proud to be part of the Amazon Web Services (AWS) Agentic AI Marketplace—Amazon’s emerging ecosystem designed to bring together agentic applications, reusable APIs, data sets, and generative AI solutions.

This integration delivers significant value, including the following:

• Seamless integration for AI workflows. Autonomous agents and builders can now readily discover and connect with Dynatrace, making it easier to compose sophisticated, intelligent workflows.
• Empower AI agents. AI agents building real-time, adaptive solutions can directly invoke Dynatrace observability, security, and automation capabilities, enabling them to make more informed decisions and take effective actions.
• Faster innovation. Enterprises benefit from faster time to value, as Dynatrace solutions are now natively composable within AWS AI services, such as Amazon Q, Amazon Bedrock, and Amazon SageMaker.

Ultimately, the AWS Agentic AI Marketplace empowers AI agents to dynamically discover, invoke, and orchestrate trusted services like Dynatrace, providing the foundation for an  autonomous and intelligent enterprise.

How Dynatrace enables business value in the agentic era

With the Dynatrace® AI-powered observability platform, organizations can benefit from the following capabilities:

• Smarter, safer automation. Dynatrace provides real-time insights into system health, anomalies, and dependencies—empowering agents to take autonomous action without compromising reliability or security.
• Accelerated AI decision-making. Dynatrace data streams feed foundational models and agentic workflows with high-fidelity, context-rich information—leading to faster, more accurate decision-making at scale.
• Personalized digital experiences. By understanding user behavior, performance trends, and business context, Dynatrace enables agents to dynamically personalize digital experiences in real time.
• Continuous optimization. From cost-aware workload placement to real-time cloud performance tuning, Dynatrace allows enterprises to scale AI workloads with confidence and efficiency.

Answering the call to innovation

The future of enterprise software is here: agentic, composable, and autonomous. For leaders navigating this massive shift, the question isn’t whether AI will play a central role, but how quickly teams can operationalize it to drive competitive advantage.

Now is the time to evaluate if your systems are truly ready—not just for AI adoption, but for AI collaboration. In this new ecosystem, it’s not simply about building AI; it’s about building with AI. This demands partners who can unlock the full potential of your data, enable your systems to adapt seamlessly, and accelerate your business outcomes. With Dynatrace, you gain a partner actively helping businesses operationalize trust, observability, and AI at scale.

The post How Dynatrace drives value in the age of AI in the AWS® Agentic AI Marketplace appeared first on Dynatrace news.

The rapid evolution of cloud technology continues to shape how businesses operate and compete. At AWS re:Invent 2024, Dynatrace showcased a suite of new AWS and Dynatrace integrations designed to enhance cloud performance, security, and automation. These innovations promise to streamline operations, boost efficiency, and offer deeper insights for enterprises using AWS services. Together, Dynatrace and AWS are paving the way for more robust and agile cloud solutions. This blog post will explore these exciting developments and what they mean for organizations.

Tried and true: AWS and Dynatrace

With over 100 out-of-the-box AWS integrations, Dynatrace and AWS have strengthened their collaboration further by bringing forth innovations that address the dynamic needs of modern enterprises. With a shared focus on enhancing cloud capabilities, this partnership underscores the significance of observability and automation in cloud environments. AWS’ recent recognition of Dynatrace as the 2024 AWS EMEA Technology Partner of the Year highlights the joint commitment to accelerate customer cloud transformation.

Dynatrace enhances support for Amazon EKS Hybrid Nodes

We are proud to announce our support for the launch of Amazon EKS Hybrid Nodes, which enable organizations to run Kubernetes workloads seamlessly across hybrid environments. With deep observability and AI-driven insights delivered by Dynatrace, customers can achieve optimal performance and reliability of their applications, whether running on-premises or in the cloud.

This integration empowers organizations to optimize their Kubernetes workloads by providing continuous observability across the entire stack, ensuring that hybrid cloud deployments are not only efficient but also resilient. Dynatrace automatic root-cause analysis and intelligent automation enables organizations to accelerate their cloud journey, reduce complexity, and achieve greater agility in their digital transformation efforts.

Streamlining observability with Dynatrace OneAgent on AWS Image Builder

In our ongoing collaboration with AWS, we’re excited to make the Dynatrace OneAgent available as a first-class integration on AWS Image Builder via the AWS Marketplace. This integration simplifies the process of embedding Dynatrace full-stack observability directly into custom Amazon Machine Images (AMIs). This integration augments our existing support for OpenTelemetry to provide customers with more flexibility.

By automating OneAgent deployment at the image creation stage, organizations can immediately equip every EC2 instance with real-time monitoring and AI-powered analytics. This seamless integration accelerates cloud adoption, allowing enterprises to maximize the value of their AWS infrastructure and focus on innovation rather than managing observability configurations.

VMware migration support for seamless transitions

For enterprises transitioning VMware-based workloads to the cloud, the process can be complex and resource-intensive. New VMware migration support from AWS simplifies this transition, reducing the challenges associated with moving critical workloads. With real-time analytics and insights, Dynatrace plays a key role in supporting organizations by providing enhanced performance, operational efficiency, and the insights needed to understand consumption and avoid over provisioning throughout the migration. This is particularly valuable for enterprises deeply invested in VMware infrastructure, as it enables them to fully harness the advantages of cloud computing.

Additionally, Dynatrace enhances the migration process by providing advanced monitoring and actionable insights, empowering businesses to minimize downtime and improve application performance. Dynatrace integrations with AWS services like AWS Application Migration Service and Migration Hub Strategy Recommendations enable a more resilient and secure approach to VMware migrations to the AWS cloud.

Automating AWS Well-Architected Framework compliance with Dynatrace and AWS CloudFormation

In collaboration with AWS, Dynatrace is introducing a new CloudFormation template that automates the validation of the AWS Well-Architected Framework, incorporating Dynatrace observability and Site Reliability Guardian to enforce quality gates within CI/CD pipelines. This solution aligns to the AWS Well-Architected Framework.

By embedding Dynatrace AI-driven observability and reliability checks into the deployment pipeline, organizations can proactively assess their cloud architectures against best practices, detecting and resolving potential issues before they impact production. This integration showcases the strength of our partnership with AWS, helping joint customers achieve cloud governance, enhance scalability, and optimize their digital applications for maximum efficiency and resilience.

Gaining precise insights with Dynatrace integration for AWS EventBridge

Now supporting a deeper integration with AWS EventBridge, Dynatrace is able to act as a consumer of AWS events. By ingesting EventBridge events into the Dynatrace platform, customers can leverage AI-powered contextual insights to gain a deeper understanding of their cloud environments.

This integration allows organizations to correlate AWS events with Dynatrace automatic dependency mapping, real-time performance monitoring, and root-cause analysis. As a result, businesses can improve incident response, optimize application performance, and streamline operations with actionable insights. This collaboration highlights the strength of the Dynatrace-AWS partnership in providing unmatched observability, enhancing service reliability, and enabling customers to innovate with confidence at cloud scale.

AWS and Dynatrace deliver transformative cloud integrations for joint customers

AWS re:Invent 2024 once again demonstrates the power of innovation in cloud technology. The new Dynatrace and AWS integrations announced at this event deliver organizations enhanced performance, security, and automation.

At Dynatrace, we are continually pushing the boundaries of what’s possible in cloud observability by partnering with AWS to deliver solutions that help organizations achieve excellence in their cloud journeys. Whether it’s simplifying Kubernetes management with EKS Hybrid Nodes, streamlining observability with simplified instrumentation on AWS Image Builder, automating compliance with the Well-Architected Framework, or gaining deeper insights with EventBridge, our integrations with AWS are designed to empower our customers to succeed.

As digital transformation continues to accelerate, Dynatrace remains committed to delivering the tools, automation, and insights organizations need to drive innovation, improve operational efficiency, and achieve superior business outcomes on AWS.

Stay tuned for more exciting updates as we continue to expand our collaboration with AWS and help our customers unlock new possibilities in the cloud.

^{Dynatrace, OneAgent, and the Dynatrace logo are trademarks of the Dynatrace, Inc. group of companies. Third-party trademarks mentioned in this blog are the property of their respective owners.}

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Organizations are increasingly migrating their on-premises workloads to the AWS cloud to reduce overhead and total cost of ownership (TCO). But while cloud migration promises to deliver better business outcomes, the process itself can prove challenging. This is because organizations need to understand what workloads they’re running on-premises today and ensure that applications continue to run seamlessly, efficiently, and securely during the migration and post-migration process.

To address these challenges, Dynatrace now offers a native integration into AWS Application Migration Service, making it the first AWS partner to be natively integrated. Dynatrace AI-powered observability enables organizations to migrate apps effectively. This observability automatically identifies performance problems, security issues, and more. Here’s how this integration between AWS Application Migration Service and Dynatrace helps organizations migrate faster, easier, and more securely.

What is AWS Application Migration Service?

AWS Application Migration Service (MGN) is a utility that enables organizations to relocate applications to the AWS cloud with minimal downtime. It uses an agent approach and offers rapid and reliable migration, ensuring applications remain fully operational during the transition. This approach helps to accelerate and optimize what was once a challenging process during migrations. Once migrated, organizations will have mirrored transitions from on-premises to their AWS cloud environment.

AWS MGN helps organizations achieve a seamless migration strategy. Here’s how it works.

• AWS Systems Manager Distributor. AWS Systems Manager Distributor (AWS SSM Distributor) enables organizations to securely distribute and install software packages, such as the Dynatrace OneAgent, across AWS and on-premises environments. It essentially facilitates the creation and distribution of software packages, the management of package versions, and the monitoring of the installation status of packages.
• AWS Replication Agent. MGN rehosts the applications to AWS by replicating block-level data from the source target to the cloud. This process can be agent-based or agentless.

The AWS Replication Agent performs an initial block-level read of the content of any volume attached to the server and replicates it to the replication server. The agent then acts as an OS-level read filter to capture writes and synchronize any block-level modifications to the AWS MGN replication server, ensuring a near-zero recovery point.

MGN and Dynatrace

The strength of the Dynatrace partnership with AWS means that organizations can now easily select the Dynatrace OneAgent installation package to add to their migrated servers via MGN post-launch actions. This integration makes Dynatrace observability automatically available to hosts as they’re migrated to the AWS cloud environment. Customers can simply select Dynatrace from the third-party post-launch actions tab and provide their Dynatrace SaaS tenant details.

Embrace AWS cloud with Dynatrace

As more organizations seek to move on-premises workloads to the AWS cloud, AWS and Dynatrace make the migration process more efficient. Together, AWS and Dynatrace assure organizations that migrated workloads will be high-performing and resilient once running in a cloud environment.

By leveraging Dynatrace intelligent observability and complete visibility into on-premises resources, teams can now marry these AI-powered insights with AWS Application Migration Service to accelerate their migration efforts. This takes the guesswork out of designing the new cloud architecture and where these workloads will run. With Dynatrace and AWS, organizations can achieve cloud migration (and thus cloud modernization) more easily so organizations can fully embrace all the benefits of the AWS cloud.

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Dynatrace is a launch partner in support of AWS Lambda Response Streaming, a new capability enabling customers to improve the efficiency and performance of their Lambda functions. This enhancement allows AWS users to stream response payloads back to clients.

Now, customers can use streamed responses to build more responsive applications by sending partial responses to clients as the response becomes available. Customers can use AWS Lambda Response Streaming to improve performance for latency-sensitive applications and return larger payload sizes. Streaming raises the default 6 MB hard limit to a 20 MB soft limit, adding greater scalability and flexibility to their applications.

What is a Lambda serverless function?

Despite being serverless, the function still requires infrastructure on which to run. The difference is the owner of the Lambda function does not have to worry about provisioning and managing servers. Lambda functions allow teams to run code for applications, back-end services, streaming processing, or any layer of the stack with less overhead. Triggering the Lambda function is event-driven and could include changes in state or an update to a file. To learn more about the AWS Lambda features, visit the Lamba features page.

What is Lambda Response Streaming?

Lambda Response Streaming enables AWS Lambda functions to stream response payloads back to clients. Customers can use response streaming to achieve the following:

• Improve Time to First Byte (TTFB) performance for latency-sensitive applications. In traditional request-response models, the response needs to be fully generated and buffered before it is returned to the client. In some cases, clients can wait several seconds before receiving the response, even though parts of the response are ready for the client to use. Response streaming lets users send partial responses back to the client as they become ready, improving TTFB latency to within milliseconds.
• Return larger payload sizes. Some applications can have payloads that are several megabytes or gigabytes in size. Response streaming lets teams transfer these payloads back to the client without having to buffer the entire payload in memory. As a result, customers can use Response Streaming to send responses as large as a 20 MB soft payload limit.

What does your Lambda function need to enhance responses?

There are a few minor changes that need to be made to how Lambda functions are written to be able to send and receive streamed responses.

• There is a new Lambda function handler signature that provides a stream object to which your function can write incoming stream data immediately, without waiting for any buffering as before.
• In order to read streamed responses, teams will need to either use the AWS SDK and call the new InvokeWithResponseStream API operation or invoke through a Function URL. The event stream will contain two characteristics: a PayloadChunk that carries the streamed data to the client and the InvokeComplete when the function has completed sending the data.

Dynatrace

The Dynatrace Software Intelligence Platform accelerates cloud operations, helping users achieve service-level objectives (SLOs) with automated intelligence and unmatched scalability. Available directly from the AWS Marketplace, Dynatrace provides full-stack observability and AI to help IT teams optimize the resiliency of their cloud applications from the user experience down to the underlying OS, infrastructure, services, and serverless functions as in Lambda. The latest Amazon Lambda innovation, Lambda Response Streaming has day-one support from Dynatrace without interruption allowing users to continue to take advantage of all the platform features. Today, the Dynatrace platform is growing to embrace the principles of advanced observability such as SecOps, Release Inventory, and our newest platform enhancement, Grail.

How does Dynatrace help?

Dynatrace provides AWS Lambda metrics monitoring in under five minutes, showing the function CPU, memory, and network health metrics all the way through to the process level. Organizations will also benefit from the visibility of new metrics directly from the Lambda Stream such as the following:

• StreamedOutboundBytes. The number of Bytes streamed out of your function.
• StreamedOutboundThroughput. The throughput in Bytes per Second.

No manual configuration is necessary. Additionally, Dynatrace auto-discovers an environment and visualizes all its dependencies, so it can determine precisely how problems evolve and how they affect the user experience. To learn more about the ease of installation, visit our support center.

If an IT team is building applications based on AWS Lambda, they need full visibility into all tiers of the stack in context to achieve the following:

• Optimize response time hotspots
• Optimize timing hotspots
• Simplify error analytics
• Understand and optimize your architecture

Automatic and intelligent end-to-end observability of AWS Lambda functions include the following:

• Seamless end-to-end distributed tracing
• Automatic observability and root-cause analysis for DevOps, cloud, and apps teams
• Insights into how serverless functions are affecting customer-facing applications
• Purpose-built with low overhead to detect function with cold start
• Built for enterprise scalability

Saving your cloud operations and site reliability engineering teams hours of guesswork and manual tagging, the Davis AI engine analyzes billions of events in real time. It also baselines the performance of an application under load, including response times, error rates, and behavior. Auto-detection starts monitoring new virtual machines as they are deployed.

Modern Cloud Done Right

As application teams modernize and adopt these latest innovations from AWS for their cloud applications, they can have the confidence that Dynatrace is delivering the answers and intelligent automation they need across their full stack to enable flawless and secure interactions, even in peak times.

Out of the box, Dynatrace also works with Amazon EC2, Elastic Container Service, Elastic Kubernetes Service, Bottlerocket, and Fargate.

Start a Dynatrace trial today on the AWS Marketplace.

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Dynatrace and the Dynatrace Intelligent Observability Platform have added support for the newly introduced Amazon VPC Flow Logs to Amazon Kinesis Data Firehose. This support enables customers to define specific endpoint delivery of real-time streaming data to platforms such as Dynatrace.

What is VPC Flow Logs?

VPC Flow Logs is an Amazon service that enables IT pros to capture information about the IP traffic that traverses network interfaces in a virtual private cloud, or VPC. By default, each record captures a network internet protocol (IP), a destination, and the source of the traffic flow that occurs within your environment.

What is Amazon VPC Flow Logs to Kinesis Data Firehose, and why is it important?

The Dynatrace platform can ingest and evaluate a large amount of data from different sources. With Amazon Web Services, the main sources from which to ingest logs—Simple Storage Service, or S3, and CloudWatch —come with an additional cost. As of today’s announcement, Amazon Web Services is adding the capability to stream these logs directly from Kinesis Data Firehose to Dynatrace, bypassing other sources from which to ingest VPC Flow Logs.

Today, delivering data to Kinesis Data Firehose — a real-time streaming mechanism that scales and delivers rich data to a Dynatrace instance — gives IT pros access to additional contextual data for your environments. In a hybrid cloud ecosystem, digital assets can reside anywhere in the cloud and on premises. The more relevant the data IT pros feed into the Dynatrace platform, the better the analysis and correlation of how VPN and other network traffic affect your environments.

As of today’s announcement, AWS and Dynatrace strengthen their commitment to their customers to continue to deliver quality and value. Additional benefits from this new Amazon feature include the following:

1. Customers can reduce operational overhead and easily process VPC Flow Logs by achieving the following:
   • The offering eliminates dependencies on custom integrations. As a result, customers can send their flow logs to Dynatrace via Kinesis Data Firehose by implementing a customer-deployed forwarding Lambda function.
   • Processing logs is now streamlined, enabling customers to enrich log metadata with insightful context that is important for delivering pipelines to single Firehose delivery streams.
2. Reduced cost of ownership
   • For customers that want to send traffic to a Dynatrace destination using Kinesis Data Firehose delivery streams, this new feature adds Kinesis Data Firehose as a destination for VPC Flow Logs, which can reduce total costs.

Why Dynatrace?

Today, Dynatrace is recognized as a Leader in the 2023 Gartner Magic Quadrant for Application Performance Monitoring and Observability.¹ When it comes to dynamic cloud environments, Dynatrace is also an innovator in “modern cloud done right.” And when it comes to Amazon, Dynatrace continues to build integrations that keep pace with today’s modernization requirements. This means customers can monitor and visualize with context how those new services affect their ecosystem and deliver the value they expect.

Dynatrace’s ingestion of logs also enables organizations to automate cloud-related log tasks by doing the following:

• automatically identifying the precise root cause of a problem in real time to simplify cloud complexity;
• automating cloud operations and triggering remediation workflow to enhance efficiency; and
• automating ingestion of logs, metrics, and traces and continuous dependency mapping with precise context across hybrid and multicloud environments.

The Dynatrace VPC Flow Log analysis capability

• Log Viewer enables users to present log data in a filterable, easy-to-use table and to browse log data within a certain time frame using detected aspects of the log content.
• Log Metrics create metrics from log data or log metadata that allow users to add to a dashboard or create custom alerting from each metric created.
• PurePath® combines logs with distributed traces, enabling users to check log records in the full context of a transaction.

The Dynatrace problem detection and analysis advantage

Dynatrace uses your data and its sophisticated AI causation engine, Davis®, to automatically detect performance anomalies in your applications, services, and infrastructure. Dynatrace-detected problems are used to report and alert on abnormal situations, such as performance degradations, improper functionality, or lack of availability — that is, problems that represent anomalies in baseline system performance. Problems have defined lifespans and are updated in real time with all incoming events and findings. Once a problem is detected, it’s listed on your problems feed.

Adding the context of VPC Flow Logs data to the correlation of the Dynatrace Davis AI engine generates a new dimension of network-specific information that can identify additional problem areas and improvement candidates. It can also understand the impact of network contribution to overall application and user health.

Resources

Learn more about VPC Flow Logs

Check out our Power Demo: Log Analytics with Dynatrace.

Learn more about how Dynatrace and AWS are “better together.”

 
1) Gartner, Magic Quadrant for Application Performance Monitoring and Observability, Gregg Siegfried, Mrudula Bangera, Matt Crossley, Padraig Byrne, 5 July 2023.
Gartner does not endorse any vendor, product, or service depicted in its research publications and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s Research & Advisory organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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Dynatrace has added support for the newly introduced Amazon Virtual Private Cloud (VPC) Flow Logs for AWS Transit Gateway. This new service enhances the user visibility of network details with direct delivery of Flow Logs for Transit Gateway to your desired endpoint via Amazon Simple Storage Service (S3) bucket or Amazon CloudWatch Logs.

What is AWS Transit Gateway?

AWS Transit Gateway is a service offering from Amazon Web Services that connects network resources via a centralized hub. This is accomplished by acting like a router and ensuring the connection is made only one time, simplifying your network.

What is VPC Flow Logs

VPC Flow Logs is a feature that gives you the capability to capture more robust IP traffic data that traverses your VPCs. Flow log data can be published to Amazon CloudWatch Logs or Amazon S3 after which you can retrieve and view its data in the Dynatrace Intelligent Observability Platform.

What can you expect from VPC Flow Logs for Transit Gateway

There are five network internet protocol (IP) characteristics that are captured within each of the Transit Gateway Flow Logs for each log source. These include Source IP, destination IP, transport protocol, source port, and destination port. There is robust metadata that is also captured as part of the flow logs and can be used for further filtering in correlation with your tracing. A full list of metrics can be found here and include dimensions such as the following:

• Packets. The number of packets transferred during the flow.
• Resource type. Type of flow log resource, Transit Gateway, or Transit Gateway attachment.
• Start. The timestamp when the first packet of the flow was received within the aggregation interval.

Why Dynatrace?

Dynatrace is a leader in cloud observability. When it comes to logs and metrics, the Dynatrace platform provides direct access to the log content of all mission-critical processes. This includes Transit Gateway. You can create custom log metrics for smarter and faster troubleshooting as well as understand log data in the context of the full stack. This includes real user impact.

Dynatrace log monitoring also allows you to automate your cloud-related log tasks so you can accomplish the following:

• Automatically see precise root cause in real time to simplify cloud complexity.
• Automate cloud operations and trigger remediation workflow to enhance efficiency.
• Automate ingestion of logs, metrics, and traces, and see continuous dependency mapping with precise context across hybrid and multicloud environments.

The Dynatrace problem-detection-and-analysis advantage

Dynatrace uses your data and its sophisticated AI causation engine Davis® to automatically detect performance anomalies in applications, services, and infrastructure. Dynatrace-detected problems are used to report and alert on abnormal situations. For example, performance degradations, improper functionality, or lack of availability (that is, problems that represent anomalies in baseline system performance). Problems have defined lifespans and are updated in real time with all incoming events and findings. Once Davis® detects a problem it lists the issue on your Problems feed.

Combining the context of Transit Gateway logs data with the correlation of our AI engine creates a new dimension of network-specific information. Teams can use this new information to identify additional problem areas and improvement candidates, as well as understand the impact of network contribution to overall application and user impact health.

The Dynatrace VPC Flow Log analysis capability

• Log Viewer. A feature that enables you to present log data in a filterable table that is easy to work with. You can also browse log data within a certain timeframe using detected aspects of the log content.
  
• Log Events. Relevant log events that are associated with problems are then factored into problem root-cause analysis.
  
• Log Metrics. Metrics from log data or log metadata that allow you to add to a dashboard or create custom alerting from each metric created.
• Purepath. The ability to check log records in the full context of a transaction by combining logs with distributed traces.
  

Conclusion

The newly introduced VPC Flow Logs for Transit Gateway service brings a new network dimension to application monitoring. This service is a step forward in giving users and adopters the tools necessary for understanding, improving, and troubleshooting network-related traffic within the context of their digital assets. Dynatrace is committed to innovation and leading the way in cloud computing. We are proud to support this feature at launch and look forward to continuing being the leader in Intelligent Observability and to build software that works perfectly.

Resources

Check out our Power Demo: Log Analytics with Dynatrace.

Learn more about how Dynatrace and AWS are “better together.”

We invite you to experience the power of the Dynatrace Software Intelligence Platform and sign up for a free 15-day trial.

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